title: An approach to absence epileptic seizures detection using Approximate Entropy.
abstract: Epilepsy is one of the most common chronic neurological diseases and the most common neurological chronic disease of childhood. The electroencephalogram (EEG) signal provides significant information neurologists take into consideration in the investigation and analysis of epileptic seizures. The Approximate Entropy (ApEn) is a formulated statistical parameter commonly used to quantify the regularity of a time series data of physiological signals. In this paper ApEn is used in order to detect the onset of epileptic seizures. The results show that the method provides promising results towards efficient detection of onset and ending of seizures, based on analyzing the corresponding EEG signals. ApEn parameters affect the method's behavior, suggesting that a more detailed study and a consistent methodology of their determination should be established. A preliminary analysis for the proper determination of these parameters is performed towards improving the results.
authors.forename: Giorgos
authors.initials: G
authors.lastname: Giannakakis
authors.forename: Vangelis
authors.initials: V
authors.lastname: Sakkalis
authors.forename: Matthew
authors.initials: M
authors.lastname: Pediaditis
authors.forename: Christina
authors.initials: C
authors.lastname: Farmaki
authors.forename: Pelagia
authors.initials: P
authors.lastname: Vorgia
authors.forename: Manolis
authors.initials: M
authors.lastname: Tsiknakis
mesh_terms: D000465:Algorithms; D001699:Biometry; D002648:Child; D002675:Child, Preschool; D004569:Electroencephalography; D019277:Entropy; D004832:Epilepsy, Absence / Q000175:diagnosis* / Q000503:physiopathology*; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D015203:Reproducibility of Results; D012640:Seizures; D012815:Signal Processing, Computer-Assisted